With the rapid development of intelligent driving and on-board intelligent applications, the computing power of on-board units is gradually inadequate. Intelligent networked vehicles offloading tasks to cloud servers through the Internet of Vehicles is considered to be a promising method. However, long distance deployment of cloud servers and the instability of return links also bring high time delay. While Mobile Edge Computing (MEC) effectively solves this problem by deploying computing resources to the network edge. Therefore, based on the idea of mobile edge computing, this paper first constructs the local edge collaborative computing model. By comprehensively considering the factors such as user psychology, vehicle speed, acceleration, location, communication ability and computing ability, the utility function of task vehicle and service vehicle is established. Then, according to the Stackelberg game strategy, the interaction behavior between task vehicle and service vehicle is modeled, the Stackelberg cyclic iterative task offloading algorithm in the Internet of Vehicles environment is proposed. It is proved that there is a Nash equilibrium point between service vehicle and task vehicle. Finally, the simulation results show that the algorithm has achieved a balance between task delay and expense, task vehicle utility and service vehicle utility, and has higher performance than other algorithms.
{"title":"Multi-master and multi-slave oriented task offloading strategy for real time and low power Internet of Vehicles","authors":"Jie Yang","doi":"10.1117/12.2667738","DOIUrl":"https://doi.org/10.1117/12.2667738","url":null,"abstract":"With the rapid development of intelligent driving and on-board intelligent applications, the computing power of on-board units is gradually inadequate. Intelligent networked vehicles offloading tasks to cloud servers through the Internet of Vehicles is considered to be a promising method. However, long distance deployment of cloud servers and the instability of return links also bring high time delay. While Mobile Edge Computing (MEC) effectively solves this problem by deploying computing resources to the network edge. Therefore, based on the idea of mobile edge computing, this paper first constructs the local edge collaborative computing model. By comprehensively considering the factors such as user psychology, vehicle speed, acceleration, location, communication ability and computing ability, the utility function of task vehicle and service vehicle is established. Then, according to the Stackelberg game strategy, the interaction behavior between task vehicle and service vehicle is modeled, the Stackelberg cyclic iterative task offloading algorithm in the Internet of Vehicles environment is proposed. It is proved that there is a Nash equilibrium point between service vehicle and task vehicle. Finally, the simulation results show that the algorithm has achieved a balance between task delay and expense, task vehicle utility and service vehicle utility, and has higher performance than other algorithms.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127780360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Qin, Yueyi Liu, B. Yang, Juan Luo, Yan Zhang, Yihan Liao, Zihan Wang
The medical intervention after a disaster event is the focus of government and citizen's attention in the public health and clinical medicine industries. For breast cancer, which has a high incidence in Russia and Belarus, the author combines the IoT management system and big data analysis to build a model of the framework and a preventive medicine system for a framework analysis and outlook.
{"title":"IoT and big data analysis based prevention and intervention network system for breast cancer susceptible people","authors":"Y. Qin, Yueyi Liu, B. Yang, Juan Luo, Yan Zhang, Yihan Liao, Zihan Wang","doi":"10.1117/12.2668220","DOIUrl":"https://doi.org/10.1117/12.2668220","url":null,"abstract":"The medical intervention after a disaster event is the focus of government and citizen's attention in the public health and clinical medicine industries. For breast cancer, which has a high incidence in Russia and Belarus, the author combines the IoT management system and big data analysis to build a model of the framework and a preventive medicine system for a framework analysis and outlook.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129597805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the widespread use of sensing elements in commercial equipment, action recognition technology is required to be more practical in people's life, especially the stable and accurate recognition. Among them, using sliding window for motion perception is an effective recognition method. However, most of the current recognition models are designed for a single action, which not only has poor recognition stability, but also cannot effectively recognize the action. This paper presents a method of action recognition based on adaptive window and broad learning, and designs an action recognition system EVM, the system effectively preprocesses the action data and realizes the accurate recognition of actions. Firstly, EVM smooth the source action data. Then, this paper proposes an extreme value filtering method to avoid the interference of peak/valley extreme points and ensures the effectiveness of action division through the adaptive window. Finally, a recognition model based on broad learning is used to classify action behaviors. According to the comparison and verification of a large number of experiments, the EVM system has a recognition accuracy as high as 97.91%, which is much better and faster than the CNN model.
{"title":"Activity recognition based on adaptive window and broad learning","authors":"Zhipeng Yu, Licai Zhu","doi":"10.1117/12.2667712","DOIUrl":"https://doi.org/10.1117/12.2667712","url":null,"abstract":"With the widespread use of sensing elements in commercial equipment, action recognition technology is required to be more practical in people's life, especially the stable and accurate recognition. Among them, using sliding window for motion perception is an effective recognition method. However, most of the current recognition models are designed for a single action, which not only has poor recognition stability, but also cannot effectively recognize the action. This paper presents a method of action recognition based on adaptive window and broad learning, and designs an action recognition system EVM, the system effectively preprocesses the action data and realizes the accurate recognition of actions. Firstly, EVM smooth the source action data. Then, this paper proposes an extreme value filtering method to avoid the interference of peak/valley extreme points and ensures the effectiveness of action division through the adaptive window. Finally, a recognition model based on broad learning is used to classify action behaviors. According to the comparison and verification of a large number of experiments, the EVM system has a recognition accuracy as high as 97.91%, which is much better and faster than the CNN model.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117220385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X-ray imaging is already a very mature technology. It is cheap and the radiation dose to the patient is very low. However, x-ray imaging can only provide two-dimensional information, not three-dimensional information of the patient's body. Computed Tomography (CT) can provide spatial information about the interior of the human body, giving the doctor more useful information, and the radiation dose to the patient is significantly higher. This is because conventional CT imaging techniques require a lot of X-rays for whole-body scanning. We introduce an end-to-end Generative Adversarial Network (GAN) network approach, AIACT-GAN, for the reconstruction of lung CT volumes directly from biplane x-ray images. In this work we reconstructed the CT in the presence of low radiation. We extracted features using a dynamic attention module and a dense connectivity module. In addition, in the fusion part we incorporated a contextual fusion module. The experimental results show that high quality CT can be reconstructed from x-ray images using AIACT-GAN.
{"title":"AIACT-GAN: CT reconstruction based on dynamic attention and generative adversarial networks","authors":"Yufeng Wang, Hongwen Liu, X. Lv","doi":"10.1117/12.2667487","DOIUrl":"https://doi.org/10.1117/12.2667487","url":null,"abstract":"X-ray imaging is already a very mature technology. It is cheap and the radiation dose to the patient is very low. However, x-ray imaging can only provide two-dimensional information, not three-dimensional information of the patient's body. Computed Tomography (CT) can provide spatial information about the interior of the human body, giving the doctor more useful information, and the radiation dose to the patient is significantly higher. This is because conventional CT imaging techniques require a lot of X-rays for whole-body scanning. We introduce an end-to-end Generative Adversarial Network (GAN) network approach, AIACT-GAN, for the reconstruction of lung CT volumes directly from biplane x-ray images. In this work we reconstructed the CT in the presence of low radiation. We extracted features using a dynamic attention module and a dense connectivity module. In addition, in the fusion part we incorporated a contextual fusion module. The experimental results show that high quality CT can be reconstructed from x-ray images using AIACT-GAN.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123111480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The long-term prediction of stock prices provides an important reference for quantitative investment decisions. Aiming at the problem of insufficient accuracy of long-term series prediction in existing stock forecasting models, this paper proposes a long-term stock price series forecasting method based on PSO-Informer. First, 43 kinds of technical indicator factors and K-line data were selected to construct the input data, and then the PSO-Informer model was used to predict the future 60 time points of the stock closing price. In the model training process, the particle swarm algorithm is used to optimize the parameters of the Informer network. Based on the five-minute K-line data of the SSE 50 stock index and the CSI 300 stock index, experimental research was conducted respectively. Taking the accuracy of the long-term stock price prediction overall trend as the evaluation index, and the prediction accuracy reaches 68.2% and 67.5% respectively. The comparison experiments with ARIMA, Prophet, PSO-LSTM and Informer prediction models show that the model has the best performance and is practical.
{"title":"Long-term stock price forecast based on PSO-informer model","authors":"H. Liu, Deng Chen, Wei Wei, Ziqiang Wei","doi":"10.1117/12.2667720","DOIUrl":"https://doi.org/10.1117/12.2667720","url":null,"abstract":"The long-term prediction of stock prices provides an important reference for quantitative investment decisions. Aiming at the problem of insufficient accuracy of long-term series prediction in existing stock forecasting models, this paper proposes a long-term stock price series forecasting method based on PSO-Informer. First, 43 kinds of technical indicator factors and K-line data were selected to construct the input data, and then the PSO-Informer model was used to predict the future 60 time points of the stock closing price. In the model training process, the particle swarm algorithm is used to optimize the parameters of the Informer network. Based on the five-minute K-line data of the SSE 50 stock index and the CSI 300 stock index, experimental research was conducted respectively. Taking the accuracy of the long-term stock price prediction overall trend as the evaluation index, and the prediction accuracy reaches 68.2% and 67.5% respectively. The comparison experiments with ARIMA, Prophet, PSO-LSTM and Informer prediction models show that the model has the best performance and is practical.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114177319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yating Gao, Xingjie Huang, Jinmeng Zhao, Jing Zhang, Xinyu Liu
The rapid development of edge network devices has led to the explosive growth of their data, and the difficulty of dealing with heterogeneous data in edge devices has been further increased. To solve the problem of heterogeneous data fusion without interaction, this paper proposes a data heterogeneous model analysis based on federated learning. Preprocess the multi-source heterogeneous data to obtain the main features of the condensed data. Then, the multi-source heterogeneous data nodes are positioned to avoid multi-fusion results, and Spatio-temporal correlation degree of the multi-source heterogeneous data is calculated to improve the accuracy of fusion. Finally, a multi-source heterogeneous data fusion model is established based on federated learning to ensure the security of data fusion. Compared with the traditional model, the data fusion of the proposed model is more stable, and the error is smaller. The effectiveness of the proposed model is verified by the stability and accuracy of the fusion of the heterogeneous data. The multi-source heterogeneous data fusion model studied in this paper can improve the quality of Internet of Things data and promote the development of edge devices in China.
{"title":"Analysis of heterogeneous data model based on federated learning","authors":"Yating Gao, Xingjie Huang, Jinmeng Zhao, Jing Zhang, Xinyu Liu","doi":"10.1117/12.2668315","DOIUrl":"https://doi.org/10.1117/12.2668315","url":null,"abstract":"The rapid development of edge network devices has led to the explosive growth of their data, and the difficulty of dealing with heterogeneous data in edge devices has been further increased. To solve the problem of heterogeneous data fusion without interaction, this paper proposes a data heterogeneous model analysis based on federated learning. Preprocess the multi-source heterogeneous data to obtain the main features of the condensed data. Then, the multi-source heterogeneous data nodes are positioned to avoid multi-fusion results, and Spatio-temporal correlation degree of the multi-source heterogeneous data is calculated to improve the accuracy of fusion. Finally, a multi-source heterogeneous data fusion model is established based on federated learning to ensure the security of data fusion. Compared with the traditional model, the data fusion of the proposed model is more stable, and the error is smaller. The effectiveness of the proposed model is verified by the stability and accuracy of the fusion of the heterogeneous data. The multi-source heterogeneous data fusion model studied in this paper can improve the quality of Internet of Things data and promote the development of edge devices in China.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115299751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The impacts of the Xinhengsha Reclamation Project on saltwater intrusion and freshwater resources in the Yangtze Estuary is simulated by Ecom-si. It is found that the salinity in the North Branch decreases slightly, the salinity in the North Channel and South Channel decreases obviously after the implementation of the Xinhengsha Reclamation Project. In addition, the implementation of the Xinhengsha Reclamation Project has a significant impact on the water intaking of the three reservoirs (Dongfengxisha Reservoir, Chenhang Reservoir, Qingcaosha Reservoir). In a spring-neap tide cycle, after the implementation of the Xinhengsha Reclamation Project, Dongfengxisha Reservoir is shortened from a maximum of 7 days before the project to 6 days, Chenhang Reservoir is shortened from 3.5 days to 0 days, Qingcaosha Reservoir is shortened from 4 days to 2.5 days. The above research results show that the implementation of the Xinhengsha Reclamation Project is beneficial to the water intaking of the three reservoirs.
{"title":"Numerical simulation study on impacts of the Xinhengsha reclamation project on salinity in the Yangtze Estuary","authors":"H. Lyu, Junjie Bian, Yu-Duo Hao, Runli Tao","doi":"10.1117/12.2667692","DOIUrl":"https://doi.org/10.1117/12.2667692","url":null,"abstract":"The impacts of the Xinhengsha Reclamation Project on saltwater intrusion and freshwater resources in the Yangtze Estuary is simulated by Ecom-si. It is found that the salinity in the North Branch decreases slightly, the salinity in the North Channel and South Channel decreases obviously after the implementation of the Xinhengsha Reclamation Project. In addition, the implementation of the Xinhengsha Reclamation Project has a significant impact on the water intaking of the three reservoirs (Dongfengxisha Reservoir, Chenhang Reservoir, Qingcaosha Reservoir). In a spring-neap tide cycle, after the implementation of the Xinhengsha Reclamation Project, Dongfengxisha Reservoir is shortened from a maximum of 7 days before the project to 6 days, Chenhang Reservoir is shortened from 3.5 days to 0 days, Qingcaosha Reservoir is shortened from 4 days to 2.5 days. The above research results show that the implementation of the Xinhengsha Reclamation Project is beneficial to the water intaking of the three reservoirs.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128469929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The morphology of galaxies can reflect the physical properties of galaxies themselves, and the classification of their morphology plays an important role in the subsequent analysis and research.In this paper, we use the photometry image of galaxy in GalaxyZoo2, select the data set according to the threshold and perform data augmentation, and apply ResNeXt to the classification of galaxy morphology, which realizes the automatic extraction, recognition and classification of galaxy morphological features.Based on the results of ResNeXt's galaxy morphology classification, five groups of comparative experiments are carried out.The five groups of comparison experiments include comparing different versions of ResNeXt model, comparing classical convolutional neural network model, comparing the latest image classification model in the last two years, comparing the simplest convolutional neural network model, and comparing the human eye.The experimental results show that the galaxy morphology classification accuracy based on ResNeXt101 network model is the highest.
{"title":"Galaxy morphology classification based on ResNeXt","authors":"Yang Yu","doi":"10.1117/12.2667464","DOIUrl":"https://doi.org/10.1117/12.2667464","url":null,"abstract":"The morphology of galaxies can reflect the physical properties of galaxies themselves, and the classification of their morphology plays an important role in the subsequent analysis and research.In this paper, we use the photometry image of galaxy in GalaxyZoo2, select the data set according to the threshold and perform data augmentation, and apply ResNeXt to the classification of galaxy morphology, which realizes the automatic extraction, recognition and classification of galaxy morphological features.Based on the results of ResNeXt's galaxy morphology classification, five groups of comparative experiments are carried out.The five groups of comparison experiments include comparing different versions of ResNeXt model, comparing classical convolutional neural network model, comparing the latest image classification model in the last two years, comparing the simplest convolutional neural network model, and comparing the human eye.The experimental results show that the galaxy morphology classification accuracy based on ResNeXt101 network model is the highest.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114622144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Collaborative knowledge innovation activities are conducive to the rapid development of knowledge economy. However, because the collaborative knowledge innovation is usually hidden in the complex network information transmission process, the efficiency and quality of knowledge innovation behaviors may be greatly affected. We take collaborative innovation participants, collaborative innovation teams and collaborative innovation achievements as the constituent elements and construct the research framework of collaborative knowledge innovation mode mining. Then, we use Apriori algorithm to mine the collaborative knowledge innovation mode and obtain the transformation mode. The results show that when the main contributors to a project are high active users, the project has a greater probability of showing a trend of high innovation activity and is more likely to become a high-quality project. The findings will help to improve the collaborative knowledge innovation ability of online community platforms and the efficiency of knowledge diffusion.
{"title":"Research on collaborative knowledge innovation mode mining based on user behavior in open source community","authors":"Jun Wang, Hongde Liu, Yani Wang, Xinyu Liang","doi":"10.1117/12.2667729","DOIUrl":"https://doi.org/10.1117/12.2667729","url":null,"abstract":"Collaborative knowledge innovation activities are conducive to the rapid development of knowledge economy. However, because the collaborative knowledge innovation is usually hidden in the complex network information transmission process, the efficiency and quality of knowledge innovation behaviors may be greatly affected. We take collaborative innovation participants, collaborative innovation teams and collaborative innovation achievements as the constituent elements and construct the research framework of collaborative knowledge innovation mode mining. Then, we use Apriori algorithm to mine the collaborative knowledge innovation mode and obtain the transformation mode. The results show that when the main contributors to a project are high active users, the project has a greater probability of showing a trend of high innovation activity and is more likely to become a high-quality project. The findings will help to improve the collaborative knowledge innovation ability of online community platforms and the efficiency of knowledge diffusion.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121596206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To make computers understand the molecules, the first and important thing is to represent molecules in a proper way, which will affect the efficiency of chemistry tasks like property prediction and molecular design. In this work, we introduce a molecular representation for noncrystalline small molecules based on the theory of quantum physics. This representation captures the microscopic spatial structure of the molecule, which ensures it reflects more visual perception information about the molecule. We use Drug3DNet as our baseline and test the efficiency of our representation. By comparing with several other representations, we prove that our representation performs better on most of the properties.
{"title":"VisMole: a molecular representation based on voxel for molecular property prediction","authors":"Qiang Tong, Jiahao Shen, Xiulei Liu","doi":"10.1117/12.2667694","DOIUrl":"https://doi.org/10.1117/12.2667694","url":null,"abstract":"To make computers understand the molecules, the first and important thing is to represent molecules in a proper way, which will affect the efficiency of chemistry tasks like property prediction and molecular design. In this work, we introduce a molecular representation for noncrystalline small molecules based on the theory of quantum physics. This representation captures the microscopic spatial structure of the molecule, which ensures it reflects more visual perception information about the molecule. We use Drug3DNet as our baseline and test the efficiency of our representation. By comparing with several other representations, we prove that our representation performs better on most of the properties.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"412 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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